Autologous Fat Grafting as a Stand-alone Method for Immediate Breast Reconstruction After Radical Mastectomy in a Series of 15 Patients
Bibliographic record
Abstract
OBJECTIVE: To date, breast reconstruction after mastectomy essentially uses flap- or prosthetic-based surgery. Autologous fat grafting (AFT) largely used in breast conservative surgery is considered an additional technique in breast reconstruction. The aim of this retrospective study was to report our experience of AFT as a stand-alone method for immediate breast reconstruction. PATIENTS AND METHODS: Fifteen patients requiring a radical mastectomy underwent AFT for immediate reconstruction since 2014. Previous breast irradiation was not a contraindication. Procedures, complications, and cosmetic results were retrospectively analyzed. RESULTS: Fifteen patients with an average age of 60.5 (43-78) years were included in this retrospective study. They had a body mass index ranging from 19 to 40. Fourteen had a mastectomy for cancer and 1 for prophylaxis. Nine received breast irradiation (7 before surgery and 2 adjuvant). A mean of 3 (2-6) AFT procedures were required to achieve total breast reconstruction. Except for the first transfer, others were performed as outpatient surgeries. Only 2 minor complications (1 hematoma and 1 abscess) not impairing results were reported. The results after a mean follow-up of 26 months were considered by the patients and surgeon as highly satisfactory even in previously irradiated breast, as assessed using a qualitative scoring analysis. CONCLUSIONS: Autologous fat grafting as a stand-alone method for immediate breast reconstruction after radical mastectomy is a safe procedure with very consistent results even for patients requiring radiation therapy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".